Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

Viana, Cláudia M.; Girão, Inês; Rocha, Jorge

doi:10.3390/rs11091104

Cited by 93 publications

(46 citation statements)

References 74 publications

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“…For example, Manabe et al [67] studied the temporal patterns of cropland using EVI time-series data and achieved classification based on the TWDTW method. Viana et al [68] combined NDVI and normalized difference water index (NDWI) and used them to identify the characteristics of land-cover types and developed land cover maps. Because all vegetation indices could not be explored at this time, the current study was limited to the NDVI.…”

Section: Identification Of Temporal Patternsmentioning

confidence: 99%

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Cheng

Wang

2019

Forests

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Efficient methodologies for mapping forest types in complicated mountain areas are essential for the implementation of sustainable forest management practices and monitoring. Existing solutions dedicated to forest-type mapping are primarily focused on supervised machine learning algorithms (MLAs) using remote sensing time-series images. However, MLAs are challenged by complex and problematic forest type compositions, lack of training data, loss of temporal data caused by clouds obscuration, and selection of input feature sets for mountainous areas. The time-weighted dynamic time warping (TWDTW) is a supervised classifier, an adaptation of the dynamic time warping method for time series analysis for land cover classification. This study evaluates the performance of the TWDTW method that uses a combination of Sentinel-2 and Landsat-8 time-series images when applied to complicated mountain forest-type classifications in southern China with complex topographic conditions and forest-type compositions. The classification outputs were compared to those produced by MLAs, including random forest (RF) and support vector machine (SVM). The results presented that the three forest-type maps obtained by TWDTW, RF, and SVM have high consistency in spatial distribution. TWDTW outperformed SVM and RF with mean overall accuracy and mean kappa coefficient of 93.81% and 0.93, respectively, followed by RF and SVM. Compared with MLAs, TWDTW method achieved the higher classification accuracy than RF and SVM, with even less training data. This proved the robustness and less sensitivities to training samples of the TWDTW method when applied to mountain forest-type classifications.

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Section: Identification Of Temporal Patternsmentioning

confidence: 99%

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Cheng

Wang

2019

Forests

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“…RS provides the opportunity for rapid acquisition of information on LULC at a much reduced price compared to the other methods like ground surveys [33,34]. The satellite images have the advantages of multi-temporal availability as well as large spatial coverage for the LULC mapping [35,36]. In the past few decades, studies on mapping, monitoring and forecasting of LULC dynamics have been carried out using medium-and low-resolution observations from satellites, such as Landsat, Satellite Pour l'Observation de la Terre, or Satellite for observation of Earth (SPOT), Indian Remote Sensing (IRS) Satellite Resourcesat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Moderate Resolution Imaging Spectroradiomete (MODIS) and others [18,31,[37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

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“…On the basis of the service time and operational status of the Landsat satellites, we chose a set of images to prepare the high-quality imagery for each year (Table 1) after the removal of the clouds and compositing operation. The Weka k-means clustering algorithm with 30 clusters was used to cluster the satellite image exporting preliminary classification [52][53][54]. Merging or reclustering and remapping the cluster numbers resulted in the 7 final land use maps.…”

Section: Data and Classificationmentioning

confidence: 99%

Assessing Sponge Cities Performance at City Scale Using Remotely Sensed LULC Changes: Case Study Nanjing

Liu

Zevenbergen

et al. 2021

Remote Sensing

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As a result of high-density urbanization and climate change, both the frequency and intensity of extreme urban rainfall are increasing. Drainage systems are not designed to cope with this increase, and as a result, floods are becoming more common in cities, particularly in the rapidly growing cities of China. To better cope with more frequent and severe urban flooding and to improve the water quality of stormwater runoff, the Chinese government launched the national Sponge City Construction (SCC) program in 2014. The current SCC design standards and guidelines are based on static values (e.g., return periods, rainfall intensities, and volume capture ratio (VCR)). They do not fully acknowledge the large differences in climate conditions across the country and assume that the hydraulic conditions will not change over time. This stationary approach stems from the traditional engineering approach designed for grey infrastructure (following a “one size fits all” approach). The purpose of this study was to develop a methodology to assess the VCR baseline (before construction in the pre-development stage) and changes in VCR (difference between the VCR of the pre- and post-development stage). The VCR of the post-development stage is one of the required indicators of the Assessment Standard for Sponge Cities Effects to evaluate SCC projects. In this study, the VCR was derived from remote-sensing-based land use land cover (LULC) change analysis, applying an unsupervised classification algorithm on different Landsat images from 1985 to 2015. A visualization method (based upon Sankey chart, which depicts the flows and their proportions of components) and a novel and practical partitioning method for built-up regions were developed to visualize and quantify the states and change flows of LULC. On the basis of these findings, we proposed a new indicator, referred to as VCRa-L, in order to assess the changes in urban hydrology after SCC construction. This study employed the city of Nanjing as a case study and analyzed detailed information on how LULC changes over time of built-up areas. The surface area of the urban and built-up areas of Nanjing quadrupled from 11% in 1985 to 44% in 2015. In the same period, neither the entire city nor its subregions reached the VCR target of 80%. The proposed new methodology aims to support national, regional, and city governments to identify and prioritize where to invest and implement SCC measures more effectively in cities across China.

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Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

Cited by 93 publications

References 74 publications

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Assessing Sponge Cities Performance at City Scale Using Remotely Sensed LULC Changes: Case Study Nanjing

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